Book Image

Hands-On Deep Learning with Apache Spark

By : Guglielmo Iozzia
Book Image

Hands-On Deep Learning with Apache Spark

By: Guglielmo Iozzia

Overview of this book

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.
Table of Contents (19 chapters)
Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark

Deploying on a Distributed System

The upcoming chapters of this book will show what we have learned so far in order to implement some practical and real-world use cases of CNNs and RNNs. But before doing that, let's consider DL4J in a production environment. This chapter is divided into four main sections:

  • Some considerations about the setup for a DL4J environment in production, with focus in particular on memory management, CPU, and GPU setup, and job submission for training
  • Distributed training architecture details (data parallelism and strategies implemented in DL4J)
  • The practical way to import, train, and execute Python (Keras and TensorFlow) models in a DL4J (JVM)-based production environment
  • A comparison between DL4J and a couple of alternative DL frameworks for the Scala programming language (with particular focus on their readiness for production)
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